meilisearch.models package

Submodules

meilisearch.models.document module

class meilisearch.models.document.Document(doc: Dict[str, Any])

Bases: object

class meilisearch.models.document.DocumentsResults(resp: Dict[str, Any])

Bases: object

meilisearch.models.embedders module

class meilisearch.models.embedders.Distribution(*args: Any, **kwargs: Any)

Bases: CamelBase

Distribution settings for embedders.

Parameters:

• mean (float) – Mean value between 0 and 1

• sigma (float) – Sigma value between 0 and 1

mean: float

sigma: float

class meilisearch.models.embedders.Embedders(*args: Any, **kwargs: Any)

Bases: CamelBase

Container for embedder configurations.

Parameters:

embedders (Dict[str, Union[OpenAiEmbedder, HuggingFaceEmbedder, OllamaEmbedder, RestEmbedder, UserProvidedEmbedder]]) – Dictionary of embedder configurations, where keys are embedder names

embedders: Dict[str, EmbedderType]

class meilisearch.models.embedders.HuggingFaceEmbedder(*args: Any, **kwargs: Any)

Bases: CamelBase

HuggingFace embedder configuration.

Parameters:

• source (str) – The embedder source, must be “huggingFace”

• url (Optional[str]) – The URL Meilisearch contacts when querying the embedder

• model (Optional[str]) – The model your embedder uses when generating vectors (defaults to BAAI/bge-base-en-v1.5)

• dimensions (Optional[int]) – Number of dimensions in the chosen model

• revision (Optional[str]) – Model revision hash

• document_template (Optional[str]) – Template defining the data Meilisearch sends to the embedder

• document_template_max_bytes (Optional[int]) – Maximum allowed size of rendered document template (defaults to 400)

• distribution (Optional[Distribution]) – Describes the natural distribution of search results

• binary_quantized (Optional[bool]) – Once set to true, irreversibly converts all vector dimensions to 1-bit values

binary_quantized: bool | None = None

dimensions: int | None = None

distribution: Distribution | None = None

document_template: str | None = None

document_template_max_bytes: int | None = None

model: str | None = None

revision: str | None = None

source: str = 'huggingFace'

url: str | None = None

class meilisearch.models.embedders.OllamaEmbedder(*args: Any, **kwargs: Any)

Bases: CamelBase

Ollama embedder configuration.

Parameters:

• source (str) – The embedder source, must be “ollama”

• url (Optional[str]) – The URL Meilisearch contacts when querying the embedder (defaults to http://localhost:11434/api/embeddings)

• api_key (Optional[str]) – Authentication token Meilisearch should send with each request to the embedder

• model (Optional[str]) – The model your embedder uses when generating vectors

• dimensions (Optional[int]) – Number of dimensions in the chosen model

• document_template (Optional[str]) – Template defining the data Meilisearch sends to the embedder

• document_template_max_bytes (Optional[int]) – Maximum allowed size of rendered document template (defaults to 400)

• distribution (Optional[Distribution]) – Describes the natural distribution of search results

• binary_quantized (Optional[bool]) – Once set to true, irreversibly converts all vector dimensions to 1-bit values

api_key: str | None = None

binary_quantized: bool | None = None

dimensions: int | None = None

distribution: Distribution | None = None

document_template: str | None = None

document_template_max_bytes: int | None = None

model: str | None = None

source: str = 'ollama'

url: str | None = None

class meilisearch.models.embedders.OpenAiEmbedder(*args: Any, **kwargs: Any)

Bases: CamelBase

OpenAI embedder configuration.

Parameters:

• source (str) – The embedder source, must be “openAi”

• url (Optional[str]) – The URL Meilisearch contacts when querying the embedder

• api_key (Optional[str]) – Authentication token Meilisearch should send with each request to the embedder

• model (Optional[str]) – The model your embedder uses when generating vectors (defaults to text-embedding-3-small)

• dimensions (Optional[int]) – Number of dimensions in the chosen model

• document_template (Optional[str]) – Template defining the data Meilisearch sends to the embedder

• document_template_max_bytes (Optional[int]) – Maximum allowed size of rendered document template (defaults to 400)

• distribution (Optional[Distribution]) – Describes the natural distribution of search results

• binary_quantized (Optional[bool]) – Once set to true, irreversibly converts all vector dimensions to 1-bit values

api_key: str | None = None

binary_quantized: bool | None = None

dimensions: int | None = None

distribution: Distribution | None = None

document_template: str | None = None

document_template_max_bytes: int | None = None

model: str | None = None

source: str = 'openAi'

url: str | None = None

class meilisearch.models.embedders.RestEmbedder(*args: Any, **kwargs: Any)

Bases: CamelBase

REST API embedder configuration.

Parameters:

• source (str) – The embedder source, must be “rest”

• url (Optional[str]) – The URL Meilisearch contacts when querying the embedder

• api_key (Optional[str]) – Authentication token Meilisearch should send with each request to the embedder

• dimensions (Optional[int]) – Number of dimensions in the embeddings

• document_template (Optional[str]) – Template defining the data Meilisearch sends to the embedder

• document_template_max_bytes (Optional[int]) – Maximum allowed size of rendered document template (defaults to 400)

• request (Dict[str, Any]) – A JSON value representing the request Meilisearch makes to the remote embedder

• response (Dict[str, Any]) – A JSON value representing the request Meilisearch expects from the remote embedder

• headers (Optional[Dict[str, str]]) – Custom headers to send with the request

• distribution (Optional[Distribution]) – Describes the natural distribution of search results

• binary_quantized (Optional[bool]) – Once set to true, irreversibly converts all vector dimensions to 1-bit values

api_key: str | None = None

binary_quantized: bool | None = None

dimensions: int | None = None

distribution: Distribution | None = None

document_template: str | None = None

document_template_max_bytes: int | None = None

headers: Dict[str, str] | None = None

request: Dict[str, Any]

response: Dict[str, Any]

source: str = 'rest'

url: str | None = None

class meilisearch.models.embedders.UserProvidedEmbedder(*args: Any, **kwargs: Any)

Bases: CamelBase

User-provided embedder configuration.

Parameters:

• source (str) – The embedder source, must be “userProvided”

• dimensions (int) – Number of dimensions in the embeddings

• distribution (Optional[Distribution]) – Describes the natural distribution of search results

• binary_quantized (Optional[bool]) – Once set to true, irreversibly converts all vector dimensions to 1-bit values

binary_quantized: bool | None = None

dimensions: int

distribution: Distribution | None = None

source: str = 'userProvided'

meilisearch.models.index module

class meilisearch.models.index.EmbedderDistribution(*args: Any, **kwargs: Any)

Bases: CamelBase

mean: float

sigma: float

class meilisearch.models.index.Faceting(*args: Any, **kwargs: Any)

Bases: CamelBase

max_values_per_facet: int

sort_facet_values_by: Dict[str, str] | None = None

class meilisearch.models.index.IndexStats(doc: Dict[str, Any])

Bases: object

class meilisearch.models.index.LocalizedAttributes(*args: Any, **kwargs: Any)

Bases: CamelBase

attribute_patterns: List[str]

locales: List[str]

class meilisearch.models.index.MinWordSizeForTypos(*args: Any, **kwargs: Any)

Bases: CamelBase

one_typo: int | None = None

two_typos: int | None = None

class meilisearch.models.index.Pagination(*args: Any, **kwargs: Any)

Bases: CamelBase

max_total_hits: int

class meilisearch.models.index.ProximityPrecision(*values)

Bases: str, Enum

BY_ATTRIBUTE = 'byAttribute'

BY_WORD = 'byWord'

class meilisearch.models.index.TypoTolerance(*args: Any, **kwargs: Any)

Bases: CamelBase

disable_on_attributes: List[str] | None = None

disable_on_words: List[str] | None = None

enabled: bool = True

min_word_size_for_typos: MinWordSizeForTypos | None = None

meilisearch.models.key module

class meilisearch.models.key.Key(*args: Any, **kwargs: Any)

Bases: _KeyBase

created_at: datetime

key: str

updated_at: datetime | None = None

validate_created_at(v: str) → datetime

validate_updated_at(v: str) → datetime | None

class meilisearch.models.key.KeyUpdate(*args: Any, **kwargs: Any)

Bases: CamelBase

actions: List[str] | None = None

description: str | None = None

expires_at: datetime | None = None

indexes: List[str] | None = None

key: str

model_config = {'ser_json_timedelta': 'iso8601'}

name: str | None = None

class meilisearch.models.key.KeysResults(*args: Any, **kwargs: Any)

Bases: CamelBase

limit: int

offset: int

results: List[Key]

total: int

meilisearch.models.task module

class meilisearch.models.task.Batch(*args: Any, **kwargs: Any)

Bases: CamelBase

details: Dict[str, Any] | None = None

duration: str | None = None

finished_at: datetime | None = None

progress: Dict[str, float | List[Dict[str, Any]]] | None = None

started_at: datetime | None = None

stats: Dict[str, int | Dict[str, Any]] | None = None

uid: int

validate_finished_at(v: str) → datetime | None

validate_started_at(v: str) → datetime | None

class meilisearch.models.task.BatchResults(*args: Any, **kwargs: Any)

Bases: CamelBase

from_: int

limit: int

next_: int | None

results: List[Batch]

total: int

class meilisearch.models.task.Task(*args: Any, **kwargs: Any)

Bases: CamelBase

canceled_by: int | None = None

details: Dict[str, Any] | None = None

duration: str | None = None

enqueued_at: datetime

error: Dict[str, Any] | None = None

finished_at: datetime | None = None

index_uid: str | None = None

started_at: datetime | None = None

status: str

type: str

uid: int

validate_enqueued_at(v: str) → datetime

validate_finished_at(v: str) → datetime | None

validate_started_at(v: str) → datetime | None

class meilisearch.models.task.TaskInfo(*args: Any, **kwargs: Any)

Bases: CamelBase

enqueued_at: datetime

index_uid: str | None

status: str

task_uid: int

type: str

validate_enqueued_at(v: str) → datetime

class meilisearch.models.task.TaskResults(resp: Dict[str, Any])

Bases: object

Module contents